1 Comparison of R with MATLAB

http://mathesaurus.sourceforge.net/octave-r.html

R (or S-Plus)	MATLAB (or Octave)	Description
`a <- -2.1; b <- 3`	`a=-2.1; b=3;`	Assignment; defining a number
`a^b`$=-9.261$	`a .^ b`	Power, $a^b$
`a != b`$=TRUE$	`a ~= b`	Not Equal
`!a`$=FALSE$	`~a` or `not(a)`	Logical NOT
`1i`	`1i` or `1j`	Imaginary unit
`abs(3+4i)` or `Mod(3+4i)`$=5$	`abs(z)`	Absolute value (modulus)
`Re(3+4i)`$=3$	`real(z)`	Real part
`Im(3+4i)`$=4$	`imag(z)`	Imaginary part
`Arg(3+4i)`$=0.9272952$	`arg(z)`	Argument (radians)
`Conj(3+4i)`$=3-4i$	`conj(z)`	Complex conjugate
`runif(10)`	`rand(1,10)`	Uniform distribution
`runif(10, min=2, max=7)`	`2+5*rand(1,10)`	Uniform: Numbers between 2 and 7
`rnorm(10)`	`randn(1,10)`	Normal distribution
`a <- c(2, 3, 4, 5)`	`a=[2, 3, 4, 5];`	Row vector, $1\times n$-matrix
`adash <- t(c(2, 3, 4, 5))`	`adash=[2, 3, 4, 5]';`	Column vector, $m\times 1$-matrix
`seq(10)` or `1:10`	`1:10`	$1,2,3,\dots,10$
`seq(0,` `length=10)`	`0:9`	$0,1,2,\dots,9$
`seq(1,10,` `by=3)`	`1:3:10`	1, 4, 7, 10
`seq(10,1)` `or 10:1`	`10:-1:1`	$10,9,8,\dots,1$
`seq(from=10, to=1, by=-3)`	`10:-3:1`	10, 7, 4, 1
`seq(1,10, length=7)`	`linspace(1,10,7)`	Linearly-spaced vector of $n=7$ points: 1, 2.5, 4, 5.5, 7, 8.5, 10
`c(a,a)`$=2, 3, 4, 5, 2, 3, 4, 5$	`[a a]`	Concatenate two vectors, end-to-end
`c(1:4,a)` $=1, 2, 3, 4, 2, 3, 4, 5$	`[1:4 a]`
`rep(a, times=2)`	`[a a]`	$2, 3, 4, 5, 2, 3, 4, 5$
`rep(a, each=3)`		$2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5$
`rep(a,a)`		$2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5$
`a[-1]` $=3, 4, 5$	`a(2:end)`	miss the first element
`a[-10]`	`a([1:9])`	miss the tenth-element (of ten)
`a[-seq(1,50,3)]` $=3, 4$		miss $1,4,7,\dots$
`a[length(a)]` $=5$	`a(end)`	last element
`a[(length(a)-1):length(a)]` $=4, 5$	`a(end-1:end)`	last two elements
`pmax(a,b)`	`max(a,b)`	pair-wise max
`max(c(a,b))`	`max([a b])`	max of all values in two vectors
`v <- max(a)` $=5$ `; i <- which.max(a)` $=4$	`[v,i] = max(a)`
`a*a` $=4, 9, 16, 25$	`a.*a`	Multiply two vectors (element-wise) $a^2$
`u %*% v`	`dot(u,v)`	Vector dot-product, $u\bullet v$
`rbind(c(2,3), c(4,5))` or `array(c(2,3,4,5),` `dim=c(2,2) )`	`a = [2 3; 4 5]`	Define a matrix
`rbind(a,b)`	`[a ; b]`	Bind rows
`ab <- cbind(a,b)`	`[a , b]`	Bind columns
`rbind(1:4, 1:4)`	`[1:4 ; 1:4]`	Bind rows (from two vectors)
`cbind(1:4,1:4)`	`[1:4 ; 1:4]'`	Bind columns (from two vectors)
`matrix(data=0,nrow=3,ncol=5)` or `array(0,c(3,5))`	`zeros(3,5)`	$0$-filled array
`matrix(1,3,5)` or `array(1,c(3,5))`	`ones(3,5)`	$1$-filled array
`as.vector(t(a))` $=2, 3, 4, 5$	`a'(:)`	Flatten to vector (by rows, like comics)
`as.vector(a)`	`a(:)`	Flatten to vector (by columns)
`a <- rbind(` `c(11, 12, 13, 14),` `c(21, 22, 23, 24),` `c(31, 32, 33, 34) )`	`a= [` `11 12 13 14 ;` `21 22 23 24 ;` `31 32 33 34 ]`	Input is a $3\times4$ array
`a[2,3]` $=23$	`a(2,3)`	Single-Element $2,3$ (row=2,col=3)
`a[1,]` $=11, 12, 13, 14$	`a(1,:)`	First row
`a[,1]` $=11, 21, 31$	`a(:,1)`	First column
`a[-1,]` $=21, 31, 22, 32, 23, 33, 24, 34$	`a(2:end,:)`	All, except first row
`a[(nrow(a)-1):nrow(a),]` $=21, 31, 22, 32, 23, 33, 24, 34$	`a(end-1:end,:)`	Last two rows
`a[(ncol(a)-1):ncol(a),]` $=13, 23, 33, 14, 24, 34$	`a(:,end-1:end)`	Last two columns
`a[,seq(1,ncol(a),by=2)]` $=11, 21, 31, 13, 23, 33$	`a(:,1:2:end)`	Strides: Every other column
`a[-2,-3]` $=11, 31, 12, 32, 14, 34$		All, except 2nd-row, and 3rd-column
`a[,-2]` $=11, 21, 31, 13, 23, 33, 14, 24, 34$	`a(:,[1 3 4])`	Remove 2nd column
`a[,1] <- 99`	`a(:,1) = 99`
`a[,1] <- c(99,98,97)`	`a(:,1) = [99 98 97]'`
`a[a>90] <- 90`	`a(a>90) = 90;`	Clipping: Replace all elements over 90
`t(a)`	`a'`	Transpose
`a=matrix(` `c(3,4,2,` `2,8,6,` `1,4,7),` `nrow=3,` `byrow=TRUE)`	`a=[3, 4, 2 ; 2, 8, 6 ; 1, 4, 7]`	(example data for sorting)
`t(sort(a))` $=1, 2, 2, 3, 4, 4, 6, 7, 8$	`sort(a(:))`	Flattened and sorted
`apply(a,2,sort)` $=1, 2, 3, 4, 4, 8, 2, 6, 7$	`sort(a)`	Sort each column
`t( apply(a,1,sort) )` $=2, 2, 1, 3, 6, 4, 4, 8, 7$	`sort(a')'`	Sort each row
	`sortrows(a,1)`	Sort rows (by first row)
`order(a)`$=3, 2, 7, 1, 4, 6, 8, 9, 5$		Sort, return indices
`apply(a,2,max)` $=3, 8, 7$	`max(a)`	max in each column
`apply(a,1,max)` $=4, 8, 7$	`max(a')`	max in each row
`max(a)` $=8$	`max(max(a))`	overall max throughout entire array
`i <- apply(a,1,` `which.max)` $=2, 2, 3$	`[v i] = max(a)`	return row-indices, i, of which row holds the max value of that column
`pmax(b,c)`	`max(b,c)`	pair-wise max
`apply(a,2, cummax)` $=3, 3, 3, 4, 8, 8, 2, 6, 7$	`cummax(a)`	cumulative max
`a[,3:1]` $=2, 6, 7, 4, 8, 4, 3, 2, 1$	`fliplr(a)`	Flip left-right
`a[3:1,]` $=1, 2, 3, 4, 8, 4, 7, 6, 2$	`flipud(a)`	Flip up-down
`dim(a)` $=3, 3$	`size(a)`	Matrix dimensions
`ncol(a)` $=3$	`size(a,2)` or `length(a)`	Number of columns
`prod(dim(a))` $=9$	`length(a(:))`	Number of elements
	`ndims(a)`	Number of dimensions
`a * b`	`a .* b`	Element-wise operations
`a %*% b`	`a * b`	Matrix product (or vector dot-product)
`outer(a,b)` or `a %o% b`		Outer product
`which(a != 2)` $=1, 3, 4, 5, 6, 8, 9$	`find(a)`	Not-equal-to-2 elements, indices
`which(a != 2,` `arr.ind=TRUE)` $=1, 3, 1, 2, 3, 2, 3, 1, 1, 2, 2, 2, 3, 3$	`[i j] = find(a)`	Not-equal-to-2 elements, array indices
`ij <- which(a != 2,` `arr.ind=TRUE);` `v <- a[ij]`	`[i j v] = find(a)`	Vector of not-equal-to-2 values
`which(a>5.5)` $=5, 8, 9$	`find(a>5.5)`	Condition, indices
`ij <- which(a>5.5,` `arr.ind=TRUE);` `v <- a[ij]` $=8, 6, 7$		Return the values
	`a .* (a>5.5)`	Only keep those elements above $5.5$, else zeroize
	`a= cat(3,` `[1 2; 1 2],` `[3 4; 3 4]);`	Define a 3-way array
	`a(1,:,:)`
`f <-` `read.table(` `"data.txt")`	`f = load('data.txt')`	Reading from a file (2d)
`plot(a,` `type="l")`	`plot(a)`	1-dimensional (line) plot
`plot(x[,1],x[,2])`	`plot(x(:,1),x(:,2),'o')`	2-dimensional scatter-plot
`plot(x1,y1);` `lines(x2,y2)`	`plot(x1,y1);` `hold on;` `plot(x2,y2)`	Overplotting: Add new plots onto current
`par(mfrow=c(2,1))`	`subplot(211)`	subplots
`plot(x,y,` `type="b",` `col="red")`	`plot(x,y,'ro-')`	Plotting symbols and color
`plot(c(1:10,10:1),` `asp=1)`	`axis equal`	1:1 aspect-ratio
`plot(x,y,` `xlim=c(0,10),` `ylim=c(0,5))`	`axis([ 0 10 0 5 ])`	Set axes-limits manually
`plot(1:10,` `main= "title",` `xlab= "x-axis",` `ylab= "y-axis")`	`title('title');` `xlabel('x-axis'); ylabel('y-axis')`	Axis labels and titles
`grid()`	`grid on`	Turn on grid lines
`plot(x,y, log="y")`	`semilogy(a)`	logarithmic y-axis
`plot(x,y, log="x")`	`semilogx(a)`	logarithmic x-axis
`plot(x,y, log="xy")`	`loglog(a)`	logarithmic x and y axes
`f <- function(x)` `sin(x/3) - cos(x/5)`	`f= inline(` `'sin(x/3) - cos(x/5)')`	Defining functions
`plot(f,` `xlim=c(0,40),` `type='p')`	`ezplot(f,[0,40]);` `fplot(` `'sin(x/3) - cos(x/5)',` `[0,40])`	Plot a function for given range
	`theta= 0:0.001:2pi;` `r= sin(2theta);`
	`polar(theta, rho)`
`hist(rnorm(1000))`	`hist(randn(1000,1))`
`hist(rnorm(1000),` `breaks= -4:4)`	`hist(randn(1000,1),` `-4:4)`
`hist(rnorm(1000),` `breaks=c(seq(-5,0,0.25),` `seq(0.5,5,0.5)),` `freq=FALSE)`
`plot( apply(a,1,sort),` `type="l")`	`plot(sort(a))`
`contour(z)`	`contour(z)`	Contour plot
`filled.contour(x,y,z,` `nlevels=7,` `color= gray.colors)`	`contourf(z);` `colormap(gray)`	Filled contour plot
`image(z,` `col= gray.colors(256))`	`image(z);` `colormap(gray)`	Plot image data
	`quiver()`	Direction-field vectors
`f <- function(x,y)` `x*exp(-x^2-y^2);` `n <- seq(-2,2,` `length=40);` `z <- outer(n,n,f)`	`n=-2:.1:2;` `[x,y]= meshgrid(n,n);` `z=x.*exp(-x.^2-y.^2);`
`persp(x,y,z,` `theta=30,` `phi=30,` `expand=0.6,` `ticktype= 'detailed')`	`mesh(z)`	Mesh plot
`persp(x,y,z,` `theta=30,` `phi=30,` `expand=0.6,` `col= 'lightblue',` `shade=0.75,` `ltheta=120,` `ticktype= 'detailed')`	`surf(x,y,z)` or `surfl(x,y,z)`	Surface plot
`lattice::cloud(z~x*y)`	`plot3(x,y,z,'k+')`	3d scatter plot
`postscript(` `file= "foo.eps");` `plot(1:10);` `dev.off()`	`plot(1:10);` `print -depsc2` `foo.eps`	PostScript
`pdf(file= 'foo.pdf')`		PDF
`svg(file= 'foo.svg')`		SVG (vector graphics for www)
`png(filename= "Rplot.png")`	`print -dpng` `foo.png`	PNG (raster-graphics)
`a <- c(1, 2, 2, 5, 2);` `b <- c(2, 3, 4)`	`a= [ 1, 2, 2, 5, 2 ];` `b= [ 2, 3, 4 ];`	Create sets
`unique(a)` $=1, 2, 5$	`unique(a)`	Set unique
`union(a,b)` $=1, 2, 5, 3, 4$	`union(a,b)`	Set union
`intersect(a,b)` $=2$	`intersect(a,b)`	Set intersection
`setdiff(a,b)` $=1, 5$	`setdiff(a,b)`	Set difference
`setdiff(` `union(a,b),` `intersect(a,b) )` $=1, 5, 3, 4$	`setxor(a,b)`	Set exclusion
`a=matrix(` `c(3,4,2,` `2,8,6,` `1,4,7),` `nrow=3,` `byrow=TRUE)`	`a=[3, 4, 2 ; 2, 8, 6 ; 1, 4, 7]`	(example data for following)
`is.element(2,a)` or `2 %in% a` $=TRUE$	`ismember(2,a)`	True for set-membership
`apply(a,2, mean)` $=2, 5.3333333, 5$	`mean(a)`	Average (column-wise)
`apply(a,2, median)` $=2, 4, 6$	`median(a)`	Median (column-wise)
`apply(a,2, sd)` $=1, 2.3094011, 2.6457513$	`std(a)`	Standard deviation (column-wise)
`apply(a,2, var)` $=1, 5.3333333, 7$	`var(a)`	Variance (column-wise)
`z <- lm(y~x);` `plot(x,y);` `abline(z)`	`z= polyval(` `polyfit(x,y,1), x);` `plot(x,y, 'o', x,z ,'-')`	Straight-line fit
`polyroot(c(1,-1,-1))`	`roots([1 -1 -1])`	Find zeros of polynomial
`fft(a)`	`fft(a)`	Fast fourier transform
`fft(a, inverse=TRUE)`	`ifft(a)`	Inverse fourier transform
`# comment here`	`% comment here`	Comment-symbol (ignore rest-of-line)
`library(graphics)`	`% must be` `in MATLABPATH`	Import library functions
`print(a)`	`disp(a)`	Print
`for(i in 1:5)` `print(i)`	`for i=1:5;` `disp(i);` `end`	for-statement
`for(i in 1:5)` `{print(i);` `print(i*2)}`	`for i=1:5,` `disp(i);` `disp(i*2);` `end`	Multi-line `for` statements
`if (1>0) a <- 100`	`if 1>0 a=100; end`	if-statement
`if (1>0) a <- 100` `else a <- 0`	`if 1>0 a=100;` `else a=0;` `end`	if-else statement

R (or S-Plus)	MATLAB (or Octave)	Description
`help.start()`	`doc`	Browse help interactively
`help()`	`help help` or `doc doc`	Help on using help
`help(plot)` or `?plot`	`help plot`	Help for a function
`help(package= 'splines')`	`help splines` or `doc splines`	Help for a toolbox/ library package
`demo()`	`demo`	Demonstration examples
`example(plot)`		Example using a function
`help.search('plot')`	`lookfor plot`	Search help files
`apropos('plot')`		Find objects by partial name
`library()`	`help`	List available packages
`find("plot")`	`which plot`	Locate functions
`methods(plot)`		List available methods for a function
`source('foo.R')`	`foo` (or `foo.m`)	Run code from file
`history()`	`history`	Command history
`savehistory(` `file= ".Rhistory")`	`diary on` [… some more code …] `diary off`	Save command history
`q(save= 'no')`	`exit` or `quit`	End session
`help(Syntax)`	`help -`	Help on operator syntax
`a %% b` $=1, 2, 1, 0, 2, 0, 0, 0, 3$	`rem(a,b)`	Remainder
`a %/% b` $=1, 0, 0, 2, 2, 1, 1, 2, 1$		Integer division
`factorial(b+1)`$=6, 24, 120$	`factorial(b+1)`	Factorial, $n!$
`a & b` $=T, T, T, T, T, T, T, T, T$	`a & b` or `and(a,b)`	Element-wise logical AND of two vectors/ matrices
`a \| b` $=T, T, T, T, T, T, T, T, T$	`a \| b` or `or(a,b)`	Element-wise logical OR of two vectors/ matrices
`a && b` $=TRUE$	`a && b`	Logical AND of two logical-values (short-circuiting)
`a \|\| b` $=TRUE$	`a \|\| b`	Logical OR of two logical-values (short-circuiting)
`any(a)` $=TRUE$	`any(a)`	True if some/any of the elements are non-zero
`all(a)`$=TRUE$	`all(a)`	True if all of the elements are non-zero
`round(a)`$=3, 2, 1, 4, 8, 4, 2, 6, 7$	`round(a)`	Round (for R: IEC60559 = nearest even)
`ceiling(a)`$=3, 2, 1, 4, 8, 4, 2, 6, 7$	`ceil(a)`	Round up
`floor(a)`$=3, 2, 1, 4, 8, 4, 2, 6, 7$	`floor(a)`	Round down
`as.integer(a)`	`fix(a)`	Round towards zero
`z <- 3+4i`	`z= 3+4i`	A complex number, $3+4\imath$
`atan2(b,a)`	`atan(a,b)`	Arctangent, $\arctan(\frac{b}{a})$
`matrix(runif(36),6)`	`rand(6)`	Uniform $6\times 6$ array
`rev(a)`$=7, 6, 2, 4, 8, 4, 1, 2, 3$	`reverse(a)`	Reverse
`a[] <- 3`	`a(:)= 3`	Set all values to same scalar-value
`matrix(9,3,5)` or `array(9,c(3,5))`	`ones(3,5)*9`	Any number-filled array
`diag(1,3)`	`eye(3)`	Identity matrix
`diag(c(4,5,6))`	`diag([4 5 6])`	Diagonal
	`magic(3)`	Magic squares; Lo Shu
`a <- matrix(1:6,` `nrow=3,` `byrow=TRUE)`	`reshape(1:6,3,2)';`	Reshaping (rows first)
`matrix(1:6, nrow=2)` or `array(1:6,c(2,3))`	`reshape(1:6,2,3);`	Reshaping (columns first)
`a[row(a) <= col(a)]`	`vech(a)`	Flatten upper triangle (by columns)
	`a([1 3],[1 4]);`	Array as indices
	`a.'` or `transpose(a)`	Non-conjugate transpose
`det(a)`	`det(a)`	Determinant
`solve(a)`	`inv(a)`	Inverse
`pracma::pinv` or `MASS::ginv`	`pinv(a)`	Pseudo-inverse
	`norm(a)`	Norms
`eigen(a` `)$values`	`eig(a)`	Eigenvalues
`eigen(` `rbind(a,` `a[,1])` `)$values`	`eig(a)`	Eigenvalues
`svd(a)$d`	`svd(a)`	Singular values
	`chol(a)`	Cholesky factorization
`eigen(a)$vectors`	`[v,l] = eig(a)`	Eigenvectors
`eigen(` `rbind(a,` `a[,1])` `)$vectors`	`[v,l] = eig(a)`	Eigenvectors
`rank(a)`	`rank(a)`	Rank
`apply(a,2,sum)`	`sum(a)`	Sum of each column
`apply(a,1,sum)`	`sum(a')`	Sum of each row
`sum(a)`	`sum(sum(a))`	Sum of all elements
`apply(a,2, cumsum)`	`cumsum(a)`	Cumulative sum (columns)
	`rot90(a)`	Rotate 90 degrees
`kronecker(` `matrix(1,2,3), a)`	`repmat(a,2,3)`	Repeat matrix: $\left[\begin{matrix} a & a & a \cr a & a & a\end{matrix} \right]$
`a[lower.tri(a)] <- 0`	`triu(a)`	Triangular, upper
`a[upper.tri(a)] <- 0`	`tril(a)`	Triangular, lower
	`[a(:), b(:)]`	Concatenate multi-dimensional matrices into one long vector
`object.size(a)` $=328$		Number of bytes used in memory
`crossprod(a,b)` or `t(a) %*% b`		Cross product
`kronecker(a,b)`	`kron(a,b)`	Kronecker product
	`a / b`	Matrix division, $b{\cdot}a^{-1}$
`solve(a,b)`	`a \ b`	Left matrix division, $b^{-1}\cdot a$ (solve linear equations)
`f <-` `read.table(` `file= "data.csv",` `sep=";")`	`x = dlmread(` `'data.csv',` `';')`	Reading fram a CSV file (2d)
`write(` `f,` `file="data.txt")`	`save -ascii data.txt f`	Writing to a file (2d)
`plot(t,s, type="n",` `xlab="",` `ylab="");` `polygon(t,s,` `col= "lightblue");` `polygon(t,c,` `col= "lightgreen")`	`fill(t,s,'b',` `t,c,'g')` `% fill has a bug?`	Filled plot
`stem(x[,3])`		Stem-and-Leaf plot
	`plot(x1,y1,` `x2,y2)`	Two graphs in one plot
`.Last.value`	`ans`	Most recent evaluated expression
`objects()`	`whos` or `who`	List variables loaded into memory
`rm(x)`	`clear x` or `clear all`	Clear variable $x$ from memory
`ifelse(a>0,a,0)`		Ternary operator (if?True:False)
`list.files()` or `dir()`	`dir` or `ls`	List files in directory
`list.files(` `pattern=".r")`	`what`	List script files in directory
`getwd()`	`pwd`	Displays the current working directory
`setwd('foo')`	`cd foo`	Change working directory
`system("notepad")`	`!notepad`	Invoke a System Command
`cor(x,y)`	`corr(x,y)`	Correlation coefficient
`cov(x,y)`	`cov(x,y)`	Covariance
`solve(a,b)`	`a= x\y`	Linear least-squares $y=ax+b$
	`polyfit(x,y,3)`	Polynomial fit
	`f= inline(` `'1/x - (x-1)');` `fzero(f,1)`	Find a zero near $x=1$
(see packages `Ryacas`, `rSymPy`)	`solve('1/x = x-1')`	Solve symbolic-equations
	`polyval([1 2 1 2],` `1:10)`	Evaluate polynomial
	`diff(a)`	Discrete difference function and approximate derivative
	`factor()`	Factorization
`.R`	`.m`	Script file extension
`string <- "a <- 234";` `eval(parse(text=string))`	`string='a=234';` `eval(string)`	Eval

See also:
- Numerical Analysis and Statistics: MATLAB, R, NumPy, Julia – a side-by-side reference sheet
- book: R and MATLAB, by David Hiebeler [@RandMATLAB2015]
- chapter: “Essential Comparisons of the MATLAB and R Languages”, within Functional Data Analysis with R and MATLAB [@Ramsay2009]

1.1 Cheatsheets

for Base R
for Advanced R
for RStudio integrated development environment (IDE)
for shiny
for R Markdown.

Figure: RMarkdown – Cheat-Sheet (https://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf)

Figure: Interactive Web-Apps with `shiny’ – Cheat-Sheet (https://www.rstudio.com/wp-content/uploads/2016/01/shiny-cheatsheet.pdf)

1.2 CRAN Task Views

Over 30 CRAN task-views allow one to browse the many packages by specialized topic:

Task-view	Description
Bayesian	Bayesian Inference
ChemPhys	Chemometrics and Computational Physics
ClinicalTrials	Clinical Trial Design, Monitoring, and Analysis
Cluster	Cluster Analysis & Finite Mixture Models
DifferentialEquations	Differential Equations
Distributions	Probability Distributions
Econometrics	Econometrics
Environmetrics	Analysis of Ecological and Environmental Data
ExperimentalDesign	Design of Experiments (DoE) & Analysis of Experimental Data
ExtremeValue	Extreme Value Analysis
Finance	Empirical Finance
Genetics	Statistical Genetics
Graphics	Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
HighPerformanceComputing	High-Performance and Parallel Computing with R
MachineLearning	Machine Learning & Statistical Learning
MedicalImaging	Medical Image Analysis
MetaAnalysis	Meta-Analysis
Multivariate	Multivariate Statistics
NaturalLanguageProcessing	Natural Language Processing
NumericalMathematics	Numerical Mathematics
OfficialStatistics	Official Statistics & Survey Methodology
Optimization	Optimization and Mathematical Programming
Pharmacokinetics	Analysis of Pharmacokinetic Data
Phylogenetics	Phylogenetics, Especially Comparative Methods
Psychometrics	Psychometric Models and Methods
ReproducibleResearch	Reproducible Research
Robust	Robust Statistical Methods
SocialSciences	Statistics for the Social Sciences
Spatial	Analysis of Spatial Data
SpatioTemporal	Handling and Analyzing Spatio-Temporal Data
Survival	Survival Analysis
TimeSeries	Time Series Analysis
WebTechnologies	Web Technologies and Services
gR	gRaphical Models in R

For developing DSP demos (e.g. with processing using FFTs, DCTs, wavelets, sound, images, symbolic math), in addition to the packages used within this PeZdemoR demo, some other R-packages that would be useful include:
- fftwtools, fftw, wavethresh, wmtsa, waveslim, wavelets, waved, dtt, seewave, monitoR, phontools, warbleR, ripa, imager, EBImage, magick, Ryacas, rSymPy, … more

This current presentation itself was produced within RStudio [@RStudio1], using
- knitr engine [@Yihui2016]
- pandoc utility [@website:pandoc1]
- convert from RMarkdown [@pkgrmarkdown] format
- into this Slidy presentation.

PeZdemoR source-code is available on GitHub, https://github.com/popeye00/inSPiRe
- Collaborations are welcomed
currently hosted (Feb/2017) at https://inspire.shinyapps.io/PeZdemoR/
- limited 25 hours per month on a no-cost account
- other hosting solutions also exist
  - online at http://shinyapps.io
  - self-served by using Linux server
  - locally-served on a single machine or Local Area Network (LAN) by a running RStudio application

1.3 The `shiny` Package

(free) contributed package to R that makes it incredibly easy to deliver interactive data summaries and queries to end users, through any web-browser
comes with a variety of widgets for quickly building user-interfaces
default styling of any shiny-generated application is based upon the default Bootstrap theme
- extensible, and it is easy to integrate a shiny-application with any web content, using HTML, CSS, and JavaScript libraries, like jQuery.js (and many others)

Comparison R with MATLAB

Peter Squires

February 20, 2017

1 Comparison of R with MATLAB

1.1 Cheatsheets

1.2 CRAN Task Views

1.3 The `shiny` Package

Comparison R with MATLAB

Peter Squires

February 20, 2017

1 Comparison of R with MATLAB

1.1 Cheatsheets

1.2 CRAN Task Views

1.3 The shiny Package

1.3 The `shiny` Package